1. Field
Embodiments relate to rate adaptive allocation of resources for delivering content over a wireless link.
2. Related Art
Stable transmission channels are required for delivery of many real-time services over wireless packet networks. These channels typically have requirements that stipulate delay, packet loss and throughput requirements. Known standards bodies (e.g., 3GPP and 3GPP2) have recognized this and provide in their respective standards mechanisms for establishing and maintaining Guaranteed Bit Rate (GBR) bearers. The logic applied to these bearers is that an optimum GBR value can be ascertained a-priori, and then used to provide a real-time service at a known quality level.
GBR has almost never been deployed by wireless service providers, largely because GBR bearers can require significant network resources when subscribers are in poor RF conditions. In addition, while the resources needed to maintain a GBR bearer may be acceptable when the network is lightly loaded, allocating significant network resources becomes increasingly less acceptable as the network congestion level increases.
Meanwhile in this environment, adaptive techniques for delivering content to and receiving content from mobile devices are becoming the norm in wireless communications. Examples include transmission of video via HTTP adaptive streaming (HAS), and voice communication via adaptive codecs such as the AMR codec used in 3GPP technologies (GSM, UMTS/HSPA, LTE). What all these techniques have in common is the ability to adapt the rate at which content is delivered to the bandwidth available on the delivery channel. The techniques provide a local optimization, with each client/server pair independently sensing the available bandwidth and adapting accordingly. However even with this ability to adapt, the quality of service delivery in wireless environments is compromised without some degree of channel stability
In adaptive streaming techniques a server provides clients with a table of URLs. Every URL points to a specific time interval of a specific quality of the same content. All intelligence is implemented in the client, the server can be any HTTP-compliant device serving regular files. Once the client has downloaded the table of URLs, the client selects the appropriate URL to fetch next. The client performs playback of the content. Typically, the client will initiate playback by selecting the smallest content chunk from those available (e.g., the chunk with the lowest available quality). This will give the fastest start-up time. The Client adjusts available quality within, for example, the network bandwidth.
The content (e.g., media file) is created in multiple qualities by, for example the server. The content is also cut into time intervals synchronously across the different qualities. Typically, every individual chunk is individually addressable by the client.
HAS is emerging as a popular approach to streaming video on demand and real-time content. HAS is adaptive in the sense that the quality of the video can be adjusted based on the bandwidth or data rate available between the server and the client. However, each client individually adapts its video quality independent of other video users sharing the same resources.
Currently there is a mismatch between the capabilities of currently used adaptive delivery protocols and standardized mechanisms to establish stable, GBR channels in wireless environments. The below example embodiments address this problem, and provide a mechanism to ensure that the resource consumed to maintain the stable channels adapts to the degree of congestion in the wireless network.
In conventional systems, the wireless network is modeled as a wireline network and issues such as channel fading, user mobility, throughput discontinuities at handoff, and Limited, shared resources are ignored. Content is delivered via adaptive techniques using best effort, bearers with each client server pair independently sensing the available bandwidth and acting accordingly. This is the typical approach today with adaptive algorithms. This approach is inadequate because variation of content delivery rate due to these issues degrades the quality of experience of the subscriber as is determined for example via Mean Opinion Scores (MOS). Because each client/server pair acts independently to maximize throughput, this approach provides for sub-optimized quality of experience. For example subscribers at the cell edge receiving such a low bit rate that the QoE is unacceptable, while subscribers near the cell antenna have bit rates that far exceed what is really necessary for a particular application.
In conventional systems, GBR bearers are established where the guaranteed bit rate value is determined a-priori. This negates the advantages of having algorithms such as HAS that can adapt to variations in available throughput. Left unconstrained this technique uses excessive amounts of air-interface resources, to the point where this technique is almost never used by wireless service providers. Further, signaling mechanisms for various aspects of the video stream, such as the device screen size, that may be utilized to optimize the resource allocation to the base station have yet to be determined.
In conventional systems typically deployed today, HAS streaming over mobile wireless is based on a best effort allocation of resources. The base station typically employs a proportional fair scheduler that is unaware of the HAS flow and treats HAS and other flows the same. Additionally a guaranteed bit rate (GBR) can be set and throughput for a particular flow can be guaranteed. A guaranteed bit rate (GBR) set equal to the fixed source codec rate of traditional streaming (RTP/UDP or HTTP/TCP progressive download) is known to improve QoE through steady offered rate as channel and load vary.